An automatic parking system provides convenience for drivers by automatically finding free parking spaces and steering their automobiles toward them. Recently, there have been increased interests in automatic parking systems. By the customers' interests and the success stories of several automatic parking systems, many car manufacturers and component manufacturers are preparing to release self-parking products.
Automatic parking system systems consist of three components: path planning (including free parking space detection), an automatic steering and braking system used to implement the planned trajectory, and the HMI (Human Machine Interface), which can be used to receive driver's input and provide visual information of the ongoing parking process.
Free parking space detection has been implemented by using various methods: the ultrasonic sensor-based method, the laser scanner-based method, the short range radar network-based method, and the vision-based method. Among these, the vision-based method has proven the most attractive to drivers because it visualizes parking situations and performs procedures to make drivers feel safer. The vision-based method can be categorized into four approaches: the parking space marking-based approach, the binocular stereo-based approach, the light stripe projection-based approach, and the monocular motion stereo and odometry-based approach.
The first approach recognizes parking space markings. Xu et al. developed color vision-based localization of parking spaces. This method uses color segmentation based on RCE neural networks, contour extraction based on the least square method, and inverse perspective transformation. Jung et al. proposed the semi-automatic parking assist system which recognized marking lines by using the Hough transform in a bird's eye view edge image captured with a wide-angle camera. In this way, target spaces can be detected with a single image at a relatively low computational cost. Also, a general configuration of a rearview camera (a single fisheye camera) can be used. However, it cannot be used when parking space markings are not visible. Also, performance can be degraded by poor visual conditions such as stains, shadows or occlusion by adjacent vehicles.
The second approach recognizes adjacent vehicles by using a binocular stereo-based 3D reconstruction method. Kaempchen et al. developed the parking space estimation system which uses a feature-based stereo algorithm, a template matching algorithm on a depth map and a 3D fitting to the 2D planar surface model of the vehicle. This approach can easily recover metric information from the fixed length of the baseline and the camera's extrinsic parameters need not be estimated every time. However, this requires extra costs and space for the equipment because a stereo camera is not a general configuration of a rearview camera. Sub-pixel accuracy is required when there are short—baselines between the two cameras, and point correspondences are difficult to find when there are wide baselines.
Jung et al. developed a method which combines the parking space marking-based approach and the binocular stereo-based approach. These researchers used obstacle depth maps for establishing the search range and simple template matching for finding the exact location of free parking spaces. This method is robust to noise factors such as stains, trash and shadows when compared to the parking space marking-based method, but it can be only used when both obstacle depth and parking space markings are available.
The third approach recognizes adjacent vehicles by using a laser projector and a single rearview camera. Jung et al. developed a method which identified free parking spaces by analyzing the light stripe (on backward objects) produced by the laser projector. This approach can be applied to dark underground parking lots and the algorithm for acquiring 3D information is relatively simple. A general configuration of a rearview camera can be used. However, this approach cannot be used during the day due to the presence of sunlight.
The fourth approach recognizes adjacent vehicles by using a monocular motion stereo method and odometry. Fintzel et al. proposed a system which provides a rendered image from a virtual viewpoint for better understanding of parking situations and procedures. This system obtains external parameters and metric information from odometry and reconstructs the 3D structure of the parking space by using point correspondences. However, these researchers did not present a free parking space detection method. A general configuration of the rearview camera can be used. However, odometry information can be erroneous when road conditions are slippery due to rain or snow.